Overview

Dataset statistics

Number of variables16
Number of observations2628
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory236.2 KiB
Average record size in memory92.0 B

Variable types

Numeric12
Categorical3
DateTime1

Alerts

monto_credito is highly correlated with saldo_capital and 3 other fieldsHigh correlation
saldo_capital is highly correlated with monto_credito and 3 other fieldsHigh correlation
saldo_Ahorro is highly correlated with monto_credito and 2 other fieldsHigh correlation
Antiguedad_en_meses is highly correlated with monto_credito and 2 other fieldsHigh correlation
plazo_dias is highly correlated with monto_credito and 1 other fieldsHigh correlation
monto_credito is highly correlated with saldo_capital and 2 other fieldsHigh correlation
saldo_capital is highly correlated with monto_credito and 2 other fieldsHigh correlation
saldo_Ahorro is highly correlated with monto_credito and 2 other fieldsHigh correlation
Antiguedad_en_meses is highly correlated with saldo_AhorroHigh correlation
plazo_dias is highly correlated with monto_credito and 1 other fieldsHigh correlation
Patrimonio is highly correlated with tasa and 1 other fieldsHigh correlation
Ingresos_Mensuales is highly correlated with tasa and 1 other fieldsHigh correlation
No_hijos is highly correlated with tasa and 1 other fieldsHigh correlation
monto_credito is highly correlated with tasa and 2 other fieldsHigh correlation
tasa is highly correlated with Patrimonio and 4 other fieldsHigh correlation
saldo_capital is highly correlated with monto_credito and 2 other fieldsHigh correlation
saldo_Ahorro is highly correlated with saldo_capital and 1 other fieldsHigh correlation
Antiguedad_en_meses is highly correlated with Max_dias_moraHigh correlation
Max_dias_mora is highly correlated with Patrimonio and 7 other fieldsHigh correlation
Id_Cliente is highly correlated with Ciudad and 1 other fieldsHigh correlation
Ciudad is highly correlated with Id_Cliente and 1 other fieldsHigh correlation
Patrimonio is highly correlated with Id_Cliente and 1 other fieldsHigh correlation
fecha_ult_desembolso is highly correlated with Ciudad and 9 other fieldsHigh correlation
monto_credito is highly correlated with fecha_ult_desembolso and 4 other fieldsHigh correlation
tasa is highly correlated with fecha_ult_desembolso and 1 other fieldsHigh correlation
saldo_capital is highly correlated with fecha_ult_desembolso and 4 other fieldsHigh correlation
saldo_Ahorro is highly correlated with fecha_ult_desembolso and 5 other fieldsHigh correlation
Antiguedad_en_meses is highly correlated with fecha_ult_desembolso and 3 other fieldsHigh correlation
Max_dias_mora is highly correlated with fecha_ult_desembolsoHigh correlation
plazo_dias is highly correlated with fecha_ult_desembolso and 3 other fieldsHigh correlation
Edad is highly correlated with fecha_ult_desembolsoHigh correlation
Ingresos_Mensuales is highly skewed (γ1 = 24.53368732) Skewed
Id_Cliente is uniformly distributed Uniform
Id_Cliente has unique values Unique
saldo_Ahorro has 109 (4.1%) zeros Zeros
Max_dias_mora has 2224 (84.6%) zeros Zeros

Reproduction

Analysis started2022-04-03 18:31:38.266292
Analysis finished2022-04-03 18:34:38.349179
Duration3 minutes and 0.08 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Id_Cliente
Real number (ℝ≥0)

HIGH CORRELATION
UNIFORM
UNIQUE

Distinct2628
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218268.5
Minimum216955
Maximum219582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-04-03T13:34:38.523178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum216955
5-th percentile217086.35
Q1217611.75
median218268.5
Q3218925.25
95-th percentile219450.65
Maximum219582
Range2627
Interquartile range (IQR)1313.5

Descriptive statistics

Standard deviation758.7825776
Coefficient of variation (CV)0.003476372347
Kurtosis-1.2
Mean218268.5
Median Absolute Deviation (MAD)657
Skewness0
Sum573609618
Variance575751
MonotonicityNot monotonic
2022-04-03T13:34:38.713179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2169661
 
< 0.1%
2174511
 
< 0.1%
2195581
 
< 0.1%
2182131
 
< 0.1%
2184651
 
< 0.1%
2187171
 
< 0.1%
2190161
 
< 0.1%
2191831
 
< 0.1%
2193201
 
< 0.1%
2194431
 
< 0.1%
Other values (2618)2618
99.6%
ValueCountFrequency (%)
2169551
< 0.1%
2169561
< 0.1%
2169571
< 0.1%
2169581
< 0.1%
2169591
< 0.1%
2169601
< 0.1%
2169611
< 0.1%
2169621
< 0.1%
2169631
< 0.1%
2169641
< 0.1%
ValueCountFrequency (%)
2195821
< 0.1%
2195811
< 0.1%
2195801
< 0.1%
2195791
< 0.1%
2195781
< 0.1%
2195771
< 0.1%
2195761
< 0.1%
2195751
< 0.1%
2195741
< 0.1%
2195731
< 0.1%

Ciudad
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
Cali
756 
Bogotá
654 
Cartagena
627 
Barranquilla
591 

Length

Max length12
Median length6
Mean length7.489726027
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBarranquilla
2nd rowBarranquilla
3rd rowBarranquilla
4th rowBarranquilla
5th rowBarranquilla

Common Values

ValueCountFrequency (%)
Cali756
28.8%
Bogotá654
24.9%
Cartagena627
23.9%
Barranquilla591
22.5%

Length

2022-04-03T13:34:38.891236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T13:34:39.000279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
cali756
28.8%
bogotá654
24.9%
cartagena627
23.9%
barranquilla591
22.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Patrimonio
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1079
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48824658.48
Minimum96817
Maximum99901000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2022-04-03T13:34:39.159837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum96817
5-th percentile6778897.05
Q124284500
median48767000
Q372493000
95-th percentile93690900
Maximum99901000
Range99804183
Interquartile range (IQR)48208500

Descriptive statistics

Standard deviation28098004.76
Coefficient of variation (CV)0.5754879938
Kurtosis-1.155972711
Mean48824658.48
Median Absolute Deviation (MAD)24256500
Skewness0.06271137545
Sum1.283112025 × 1011
Variance7.894978714 × 1014
MonotonicityNot monotonic
2022-04-03T13:34:39.344700image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4207869820
 
0.8%
8457800020
 
0.8%
5000000018
 
0.7%
1938000015
 
0.6%
1839200011
 
0.4%
775060007
 
0.3%
839000006
 
0.2%
545150006
 
0.2%
591740004
 
0.2%
107150004
 
0.2%
Other values (1069)2517
95.8%
ValueCountFrequency (%)
968171
< 0.1%
1910771
< 0.1%
2368021
< 0.1%
3271701
< 0.1%
3354481
< 0.1%
4955591
< 0.1%
6075781
< 0.1%
6527141
< 0.1%
7780711
< 0.1%
8790601
< 0.1%
ValueCountFrequency (%)
999010003
0.1%
998903401
 
< 0.1%
998330003
0.1%
998300102
0.1%
996150003
0.1%
996090003
0.1%
993990003
0.1%
993760003
0.1%
993600004
0.2%
991160003
0.1%

Ingresos_Mensuales
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct884
Distinct (%)33.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1499859.435
Minimum421263
Maximum28336624
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2022-04-03T13:34:39.548829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum421263
5-th percentile774589
Q11185693
median1493587
Q31768731
95-th percentile2166280
Maximum28336624
Range27915361
Interquartile range (IQR)583038

Descriptive statistics

Standard deviation669294.061
Coefficient of variation (CV)0.4462378576
Kurtosis983.5742844
Mean1499859.435
Median Absolute Deviation (MAD)298457
Skewness24.53368732
Sum3941630595
Variance4.479545401 × 1011
MonotonicityNot monotonic
2022-04-03T13:34:39.967824image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86108578
 
3.0%
77458740
 
1.5%
74705438
 
1.4%
77458930
 
1.1%
77485118
 
0.7%
16896694
 
0.2%
15864334
 
0.2%
17441974
 
0.2%
9605914
 
0.2%
16288794
 
0.2%
Other values (874)2404
91.5%
ValueCountFrequency (%)
4212631
 
< 0.1%
4374651
 
< 0.1%
4836331
 
< 0.1%
5211601
 
< 0.1%
5633161
 
< 0.1%
6674023
0.1%
6712413
0.1%
6771933
0.1%
6864533
0.1%
6881143
0.1%
ValueCountFrequency (%)
283366241
 
< 0.1%
23366231
 
< 0.1%
23366221
 
< 0.1%
23261442
0.1%
23246511
 
< 0.1%
23193252
0.1%
23122791
 
< 0.1%
23105782
0.1%
23087363
0.1%
23084703
0.1%

No_hijos
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
0
932 
2
872 
1
816 
3
 
7
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1
2nd row2
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0932
35.5%
2872
33.2%
1816
31.1%
37
 
0.3%
41
 
< 0.1%

Length

2022-04-03T13:34:40.123827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T13:34:40.226822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0932
35.5%
2872
33.2%
1816
31.1%
37
 
0.3%
41
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

oficina
Real number (ℝ≥0)

Distinct29
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5461.408295
Minimum4003
Maximum7064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-04-03T13:34:40.342825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4003
5-th percentile4005
Q14017
median4054
Q37030
95-th percentile7064
Maximum7064
Range3061
Interquartile range (IQR)3013

Descriptive statistics

Standard deviation1504.387868
Coefficient of variation (CV)0.2754578648
Kurtosis-1.993230415
Mean5461.408295
Median Absolute Deviation (MAD)51
Skewness0.08843011358
Sum14352581
Variance2263182.858
MonotonicityNot monotonic
2022-04-03T13:34:40.516828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4010325
12.4%
7025285
 
10.8%
7032275
 
10.5%
4024218
 
8.3%
7064197
 
7.5%
4011155
 
5.9%
4050137
 
5.2%
4005103
 
3.9%
7030101
 
3.8%
403193
 
3.5%
Other values (19)739
28.1%
ValueCountFrequency (%)
400364
 
2.4%
4005103
 
3.9%
4010325
12.4%
4011155
5.9%
40143
 
0.1%
401717
 
0.6%
4024218
8.3%
402557
 
2.2%
403082
 
3.1%
403193
 
3.5%
ValueCountFrequency (%)
7064197
7.5%
705930
 
1.1%
705120
 
0.8%
704619
 
0.7%
70443
 
0.1%
704121
 
0.8%
703919
 
0.7%
703855
 
2.1%
7032275
10.5%
7030101
 
3.8%

fecha_ult_desembolso
Date

HIGH CORRELATION

Distinct100
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
Minimum2017-01-04 00:00:00
Maximum2017-12-06 00:00:00
2022-04-03T13:34:40.697826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:40.883825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

monto_credito
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct233
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2211341.394
Minimum300000
Maximum9800000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2022-04-03T13:34:41.075497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum300000
5-th percentile500000
Q11031210
median2000000
Q32896255
95-th percentile5534420
Maximum9800000
Range9500000
Interquartile range (IQR)1865045

Descriptive statistics

Standard deviation1666275.124
Coefficient of variation (CV)0.7535132877
Kurtosis3.849787996
Mean2211341.394
Median Absolute Deviation (MAD)961115
Skewness1.784314133
Sum5811405183
Variance2.776472788 × 1012
MonotonicityNot monotonic
2022-04-03T13:34:41.265496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2000000289
 
11.0%
1500000162
 
6.2%
1000000156
 
5.9%
3000000126
 
4.8%
500000091
 
3.5%
80000087
 
3.3%
400000084
 
3.2%
50000073
 
2.8%
250000064
 
2.4%
120000053
 
2.0%
Other values (223)1443
54.9%
ValueCountFrequency (%)
30000018
0.7%
32000010
 
0.4%
3300003
 
0.1%
3465603
 
0.1%
3500006
 
0.2%
3700003
 
0.1%
40000025
1.0%
4388006
 
0.2%
4440003
 
0.1%
4465603
 
0.1%
ValueCountFrequency (%)
98000001
 
< 0.1%
950000014
0.5%
94587203
 
0.1%
900000013
0.5%
89000005
 
0.2%
80000007
0.3%
73077003
 
0.1%
72793604
 
0.2%
70000003
 
0.1%
69000009
0.3%

tasa
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct16
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.66285388
Minimum0.21
Maximum33.93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-04-03T13:34:41.421758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.3
Q133.93
median33.93
Q333.93
95-th percentile33.93
Maximum33.93
Range33.72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12.01441028
Coefficient of variation (CV)0.4191630857
Kurtosis1.734011528
Mean28.66285388
Median Absolute Deviation (MAD)0
Skewness-1.922999514
Sum75325.98
Variance144.3460544
MonotonicityNot monotonic
2022-04-03T13:34:41.546757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
33.932065
78.6%
0.3367
 
14.0%
33.1888
 
3.3%
28.7333
 
1.3%
28.0715
 
0.6%
0.2812
 
0.5%
0.2112
 
0.5%
31.896
 
0.2%
32.096
 
0.2%
21.946
 
0.2%
Other values (6)18
 
0.7%
ValueCountFrequency (%)
0.2112
 
0.5%
0.233
 
0.1%
0.253
 
0.1%
0.2812
 
0.5%
0.3367
14.0%
21.946
 
0.2%
22.63
 
0.1%
25.123
 
0.1%
28.0715
 
0.6%
28.7333
 
1.3%
ValueCountFrequency (%)
33.932065
78.6%
33.1888
 
3.3%
32.883
 
0.1%
32.273
 
0.1%
32.096
 
0.2%
31.896
 
0.2%
28.7333
 
1.3%
28.0715
 
0.6%
25.123
 
0.1%
22.63
 
0.1%

saldo_capital
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct731
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1621322.632
Minimum18714
Maximum9925519
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2022-04-03T13:34:41.711420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18714
5-th percentile239561
Q1649156
median1105952.5
Q32093885.25
95-th percentile4764789
Maximum9925519
Range9906805
Interquartile range (IQR)1444729.25

Descriptive statistics

Standard deviation1452214.048
Coefficient of variation (CV)0.8956971416
Kurtosis5.603847279
Mean1621322.632
Median Absolute Deviation (MAD)639768.5
Skewness2.060017777
Sum4260835878
Variance2.10892564 × 1012
MonotonicityNot monotonic
2022-04-03T13:34:41.973418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100000034
 
1.3%
80000029
 
1.1%
200000028
 
1.1%
150000027
 
1.0%
209312026
 
1.0%
104656025
 
1.0%
250000025
 
1.0%
66239316
 
0.6%
50000015
 
0.6%
206984015
 
0.6%
Other values (721)2388
90.9%
ValueCountFrequency (%)
187143
0.1%
406424
0.2%
759423
0.1%
793983
0.1%
942413
0.1%
949333
0.1%
958963
0.1%
968413
0.1%
992903
0.1%
1006003
0.1%
ValueCountFrequency (%)
99255193
0.1%
96473853
0.1%
90000002
0.1%
88406863
0.1%
84435993
0.1%
82530653
0.1%
78100843
0.1%
78000001
 
< 0.1%
72241903
0.1%
69155553
0.1%

saldo_Ahorro
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct784
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28469.95396
Minimum0
Maximum159843
Zeros109
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size20.7 KiB
2022-04-03T13:34:42.158325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1358
Q19474
median20631
Q339369
95-th percentile83782
Maximum159843
Range159843
Interquartile range (IQR)29895

Descriptive statistics

Standard deviation26320.43199
Coefficient of variation (CV)0.9244985793
Kurtosis3.748261407
Mean28469.95396
Median Absolute Deviation (MAD)12431.5
Skewness1.743781837
Sum74819039
Variance692765140
MonotonicityNot monotonic
2022-04-03T13:34:42.344325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0109
 
4.1%
6365813
 
0.5%
2037010
 
0.4%
2063110
 
0.4%
197139
 
0.3%
381959
 
0.3%
246429
 
0.3%
532978
 
0.3%
538907
 
0.3%
182627
 
0.3%
Other values (774)2437
92.7%
ValueCountFrequency (%)
0109
4.1%
5713
 
0.1%
6623
 
0.1%
8843
 
0.1%
9213
 
0.1%
10661
 
< 0.1%
10863
 
0.1%
11143
 
0.1%
11783
 
0.1%
13583
 
0.1%
ValueCountFrequency (%)
1598433
0.1%
1585823
0.1%
1521133
0.1%
1455412
0.1%
1374053
0.1%
1370842
0.1%
1364793
0.1%
1363433
0.1%
1325493
0.1%
1232163
0.1%

Antiguedad_en_meses
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct110
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.20890411
Minimum11
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2022-04-03T13:34:42.541324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12
Q143
median82
Q3117
95-th percentile118
Maximum120
Range109
Interquartile range (IQR)74

Descriptive statistics

Standard deviation36.80815971
Coefficient of variation (CV)0.482990277
Kurtosis-1.234072337
Mean76.20890411
Median Absolute Deviation (MAD)35
Skewness-0.3823715749
Sum200277
Variance1354.840621
MonotonicityNot monotonic
2022-04-03T13:34:42.726912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117561
 
21.3%
12201
 
7.6%
11899
 
3.8%
8281
 
3.1%
9559
 
2.2%
7748
 
1.8%
3445
 
1.7%
6244
 
1.7%
9443
 
1.6%
7842
 
1.6%
Other values (100)1405
53.5%
ValueCountFrequency (%)
1112
 
0.5%
12201
7.6%
1321
 
0.8%
149
 
0.3%
153
 
0.1%
166
 
0.2%
1722
 
0.8%
184
 
0.2%
193
 
0.1%
2013
 
0.5%
ValueCountFrequency (%)
12014
 
0.5%
11926
 
1.0%
11899
 
3.8%
117561
21.3%
11641
 
1.6%
1156
 
0.2%
1143
 
0.1%
11334
 
1.3%
1128
 
0.3%
1119
 
0.3%

Max_dias_mora
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.732115677
Minimum0
Maximum96
Zeros2224
Zeros (%)84.6%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2022-04-03T13:34:43.065956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile27
Maximum96
Range96
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.97872377
Coefficient of variation (CV)3.165333392
Kurtosis17.39820812
Mean4.732115677
Median Absolute Deviation (MAD)0
Skewness4.062429051
Sum12436
Variance224.3621658
MonotonicityNot monotonic
2022-04-03T13:34:43.252911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
02224
84.6%
2638
 
1.4%
1238
 
1.4%
1127
 
1.0%
427
 
1.0%
9025
 
1.0%
2023
 
0.9%
815
 
0.6%
1515
 
0.6%
2515
 
0.6%
Other values (39)181
 
6.9%
ValueCountFrequency (%)
02224
84.6%
427
 
1.0%
69
 
0.3%
73
 
0.1%
815
 
0.6%
93
 
0.1%
109
 
0.3%
1127
 
1.0%
1238
 
1.4%
1515
 
0.6%
ValueCountFrequency (%)
961
 
< 0.1%
931
 
< 0.1%
921
 
< 0.1%
9025
1.0%
894
 
0.2%
884
 
0.2%
833
 
0.1%
802
 
0.1%
783
 
0.1%
734
 
0.2%

plazo_dias
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct27
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean653.2191781
Minimum144
Maximum1080
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2022-04-03T13:34:43.409954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum144
5-th percentile360
Q1450
median600
Q3720
95-th percentile1080
Maximum1080
Range936
Interquartile range (IQR)270

Descriptive statistics

Standard deviation259.9916002
Coefficient of variation (CV)0.3980158712
Kurtosis-0.9399454714
Mean653.2191781
Median Absolute Deviation (MAD)150
Skewness0.3790515569
Sum1716660
Variance67595.63219
MonotonicityNot monotonic
2022-04-03T13:34:43.555977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
720659
25.1%
1080492
18.7%
540465
17.7%
360456
17.4%
450209
 
8.0%
900110
 
4.2%
30062
 
2.4%
60039
 
1.5%
18029
 
1.1%
24015
 
0.6%
Other values (17)92
 
3.5%
ValueCountFrequency (%)
14410
 
0.4%
18029
 
1.1%
2106
 
0.2%
24015
 
0.6%
2706
 
0.2%
30062
 
2.4%
3303
 
0.1%
360456
17.4%
4207
 
0.3%
450209
8.0%
ValueCountFrequency (%)
1080492
18.7%
10503
 
0.1%
10203
 
0.1%
9903
 
0.1%
9604
 
0.2%
900110
 
4.2%
8406
 
0.2%
7804
 
0.2%
7503
 
0.1%
720659
25.1%

Edad
Real number (ℝ≥0)

HIGH CORRELATION

Distinct50
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.79642314
Minimum20
Maximum69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2022-04-03T13:34:43.719308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile22
Q131
median43
Q356
95-th percentile67
Maximum69
Range49
Interquartile range (IQR)25

Descriptive statistics

Standard deviation14.21034122
Coefficient of variation (CV)0.324463511
Kurtosis-1.184097966
Mean43.79642314
Median Absolute Deviation (MAD)12
Skewness0.1050417052
Sum115097
Variance201.9337976
MonotonicityNot monotonic
2022-04-03T13:34:43.915308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40112
 
4.3%
28105
 
4.0%
2781
 
3.1%
5379
 
3.0%
3875
 
2.9%
3571
 
2.7%
2269
 
2.6%
3167
 
2.5%
6262
 
2.4%
4661
 
2.3%
Other values (40)1846
70.2%
ValueCountFrequency (%)
2038
 
1.4%
2151
1.9%
2269
2.6%
2331
 
1.2%
2434
 
1.3%
2555
2.1%
2652
2.0%
2781
3.1%
28105
4.0%
2932
 
1.2%
ValueCountFrequency (%)
6935
1.3%
6858
2.2%
6757
2.2%
6645
1.7%
6542
1.6%
6455
2.1%
6353
2.0%
6262
2.4%
6142
1.6%
6035
1.3%

Acepta_Campaña
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.7 KiB
0
1322 
1
1306 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
01322
50.3%
11306
49.7%

Length

2022-04-03T13:34:44.074309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-03T13:34:44.161314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
01322
50.3%
11306
49.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-04-03T13:34:30.610667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:31:44.771814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:56.814104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:05.009508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:13.242510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:24.679215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:36.175220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:45.366788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:54.321941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:07.271514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:16.185606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:24.057023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:35.841579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:31:55.044029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:02.653102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:10.190510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:22.479508image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:33.138224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:42.581337image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:52.309943image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:05.073516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:13.576080image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:22.242216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:28.676023image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:36.005614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:31:59.424114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:02.861104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:10.389551image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:22.649220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:33.441215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:42.767779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:52.472941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:05.334515image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:13.754042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:22.403211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:29.032981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:36.169574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:03.748108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:03.075103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:10.599511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:22.824215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:33.617215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:42.960779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:52.672942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:05.521523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:13.958041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:22.572212image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:29.191036image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:36.323614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:08.344102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:03.342103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:10.922507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:23.000216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:33.774225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:43.131783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:52.890941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:05.705525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:14.243041image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:22.773213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:29.339021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:36.480603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:13.210102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:03.524101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:11.105510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:23.173213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:33.955229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:43.319783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:53.074941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:05.917518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:14.424081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:22.933213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:29.500794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:36.643579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:19.204103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:03.741102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:11.302510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:23.357215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:34.161216image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:43.627781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:53.238986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:06.117514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:14.605079image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:23.097227image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:29.658666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:36.806617image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:25.464142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:04.126104image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:11.525510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:23.539225image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:34.435223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:43.860781image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:53.427993image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:06.288518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:14.790647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:23.261220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:29.821668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:36.976577image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:31.898102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:04.303108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:11.978518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:23.783217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:34.992219image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:44.121782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:53.639944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:06.472516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:14.982609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:23.428214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:29.984665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:37.135576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:37.718102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:04.477510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:12.326509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:23.953256image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:35.423215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:44.402793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:53.797960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:06.648520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:15.403651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:23.587223image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:30.151662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:37.283578image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:45.092099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:04.688510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:12.609513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:24.127262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:35.660290image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:44.715783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:53.976942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:06.842512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:15.643606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:23.740019image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:30.300662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:37.430586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:32:50.009103image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:04.843507image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:12.899513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:24.318217image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:35.926235image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:45.107782image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:33:54.139940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:07.013527image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:15.872609image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:23.893978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-03T13:34:30.448666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-03T13:34:44.288447image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-03T13:34:44.536769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-03T13:34:44.776728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-03T13:34:45.004728image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-03T13:34:45.187732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-03T13:34:37.738574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-03T13:34:38.142875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Id_ClienteCiudadPatrimonioIngresos_MensualesNo_hijosoficinafecha_ult_desembolsomonto_creditotasasaldo_capitalsaldo_AhorroAntiguedad_en_mesesMax_dias_moraplazo_diasEdadAcepta_Campaña
0216966Barranquilla132240002042480140242017-05-14150000033.93135738733438120900501
1216967Barranquilla133800001513835240112017-02-05150000033.933922620120540591
2216978Barranquilla173200001594497070322017-02-0710641700.302117240120540631
3216979Barranquilla174390001104406270322017-09-0523385520.308289930120720551
4217028Barranquilla413660001988538270252017-07-06238556033.9319979560120720450
5217029Barranquilla417710001690652170322017-11-0640000000.3015380558844120720551
6217044Barranquilla49007000868070070252017-04-20230000033.9320812670120720371
7217067Barranquilla578150001338413240052017-09-06120000033.183639700120450381
8217074Barranquilla614570002024862240142017-05-14200000025.121005056132549120900691
9217077Barranquilla636140001135835070252017-06-25150000033.93132922713104120450691

Last rows

Id_ClienteCiudadPatrimonioIngresos_MensualesNo_hijosoficinafecha_ult_desembolsomonto_creditotasasaldo_capitalsaldo_AhorroAntiguedad_en_mesesMax_dias_moraplazo_diasEdadAcepta_Campaña
2618219271Cartagena200800001555351140052017-04-0780000028.076802932915411990360611
2619217677Bogotá624080001613334070192017-08-0540000000.2119721764187411971080340
2620217895Bogotá62408000747054270192017-08-0540000000.2119721764187411971080341
2621218113Bogotá624080001613334270192017-08-0540000000.2119721764187411971080341
2622217716Bogotá815370002149601040032017-04-13900000033.9378100841860311901080530
2623217934Bogotá81537000747054040032017-04-13900000033.9378100841860312001080531
2624218152Bogotá815370002149601040032017-04-13890000033.9378100841860312001080531
2625217713Bogotá845780001952025240302017-04-29900000033.939000000197131200720520
2626217931Bogotá79817000747054240302017-04-29900000033.939000000197131200720521
2627218149Bogotá798170001952025240302017-04-29890000033.937800000197131200720521